Forest Types Classification Using Etm+ Data in the North of Iran/ Comparison of Object-oriented with Pixel-based Classification

Different classification methods have been used for classification of satellite data by researchers. Addition to current classification, pixel-based methods, developments in segmentation and object oriented techniques offer the suitable analyses to classify satellite data. To compare the pixel-based with object-oriented classification approaches to extract forest types, a case study in a small area have been accomplished in the northern forests of Iran. The ETM+ data and some processed bands, which extracted by suitable processing analyses, were used due to high spectral resolution. Pre-processing of data was done for geometric correction of images and corresponding to ground truth map. The best suitable data sets have been chosen by seperability indexes. In the pixel-based classification approach, the maximum likelihood classifier classified images of data set. In the object-oriented approach, images were segmented to homogenous area as forest types by suitable parameters in some level. Classification of segments was done trough three classification methods of nearest neighbor, membership function and combination of both methods. A sample ground truth map of forest type did the accuracy assessment of the results. It was generated trough sampling method by 193 plots of one hectare. The accuracy assessment of the results showed that the object-oriented classification approach could improve considerably the results in compare to pixel based classification approach (19%). However, increasing of kappa coefficient from 25.5 % in the pixel based classification to 44.4 % in the object-oriented approach shows capability of multiresolution segmentation of data, which provide other useful attributes for classification in addition to spectral information (or overall accuracy from 44 % to 61%). The results of study indicate that integration of nearest neighbor with membership function technique can improve the results more than the both techniques individually. More researches to survey on these classification techniques will be necessary in future.